D4RL: Datasets for Deep Data-Driven Reinforcement Learning (halfcheetah-medium-v2).

Canonical offline RL benchmark environment from D4RL. The halfcheetah-medium-v2 dataset contains 1M transitions collected from a medium-level SAC policy. Scores are reported as normalized return where 0 = random policy and 100 = expert SAC policy.

Best published scores.

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Primary

normalized_return · higher is better

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• 01A public checkpoint or API endpoint
• 02A reproduction script with frozen commit + seed
• 03Declared evaluation environment (Python, deps)
• 04One row per metric declared by this dataset
• 05A contact so we can follow up on discrepancies